clean up after rebasing

bloptools/bayesian/acquisition/__init__.py

@@ -1,8 +1,3 @@
-<<<<<<< HEAD
-from .agent import *  # noqa F401
-from .devices import *  # noqa F401
-from .objective import *  # noqa F401
-=======
 import os
 
 import yaml
@@ -132,4 +127,3 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func_meta = {"name": acq_func_name, "args": {}}
 
     return (acq_func, acq_func_meta) if return_metadata else acq_func
->>>>>>> 39a579f (make sure DOF bounds are cast to floats)

bloptools/bayesian/agent.py

@@ -105,7 +105,7 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
         """
         Inform the agent about new inputs and targets for the model.
 
-        If run with no arguments, it will just reconstruct all the models. 
+        If run with no arguments, it will just reconstruct all the models.
         """
 
         new_table = pd.DataFrame() if new_table is None else new_table
@@ -213,7 +213,7 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             NUM_RESTARTS = 8
             RAW_SAMPLES = 1024
 
-            candidates, acqf_objective = botorch.optim.optimize_acqf(
+            candidates, acqf_obj = botorch.optim.optimize_acqf(
                 acq_function=acq_func,
                 bounds=self.acquisition_function_bounds,
                 q=n,
@@ -226,43 +226,44 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
 
             active_dofs_are_read_only = np.array([dof.read_only for dof in self.dofs.subset(active=True)])
 
-            acquisition_X = x[..., ~active_dofs_are_read_only]
+            acq_points = x[..., ~active_dofs_are_read_only]
             read_only_X = x[..., active_dofs_are_read_only]
             acq_func_meta["read_only_values"] = read_only_X
 
         else:
-
-            acqf_objective = None
+            acqf_obj = None
 
             if acq_func_name == "random":
-                acquisition_X = torch.rand()
+                acq_points = torch.rand()
                 acq_func_meta = {"name": "random", "args": {}}
 
             if acq_func_name == "quasi-random":
-                acquisition_X = self._subset_inputs_sampler(n=n, active=True, read_only=False).squeeze(1).numpy()
+                acq_points = self._subset_inputs_sampler(n=n, active=True, read_only=False).squeeze(1).numpy()
                 acq_func_meta = {"name": "quasi-random", "args": {}}
 
             elif acq_func_name == "grid":
                 n_active_dims = len(self.dofs.subset(active=True, read_only=False))
-                acquisition_X = self.test_inputs_grid(max_inputs=n).reshape(-1, n_active_dims).numpy()
+                acq_points = self.test_inputs_grid(max_inputs=n).reshape(-1, n_active_dims).numpy()
                 acq_func_meta = {"name": "grid", "args": {}}
 
             else:
                 raise ValueError()
 
             # define dummy acqf objective
-            acqf_objective = None
+            acqf_obj = None
 
         acq_func_meta["duration"] = duration = ttime.monotonic() - start_time
 
         if self.verbose:
-            print(f"found points {acquisition_X} with acqf {acq_func_meta['name']} in {duration:.01f} seconds (obj = {acqf_objective})")
+            print(
+                f"found points {acq_points} with acqf {acq_func_meta['name']} in {duration:.01f} seconds (obj = {acqf_obj})"
+            )
 
         if route and n > 1:
-            routing_index = utils.route(self.dofs.subset(active=True, read_only=False).readback, acquisition_X)
-            acquisition_X = acquisition_X[routing_index]
+            routing_index = utils.route(self.dofs.subset(active=True, read_only=False).readback, acq_points)
+            acq_points = acq_points[routing_index]
 
-        return acquisition_X, acq_func_meta
+        return acq_points, acq_func_meta
 
     def acquire(self, acquisition_inputs):
         """
@@ -272,7 +273,7 @@ def acquire(self, acquisition_inputs):
         """
         try:
             acquisition_devices = self.dofs.subset(active=True, read_only=False).devices
-            #read_only_devices = self.dofs.subset(active=True, read_only=True).devices
+            # read_only_devices = self.dofs.subset(active=True, read_only=True).devices
 
             # the acquisition plan always takes as arguments:
             # (things to move, where to move them, things to trigger once you get there)
@@ -356,12 +357,12 @@ def reset(self):
     def benchmark(
         self, output_dir="./", runs=16, n_init=64, learning_kwargs_list=[{"acq_func": "qei", "n": 4, "iterations": 16}]
     ):
-        cache_limits = {dof.name:dof.limits for dof in self.dofs}
+        cache_limits = {dof.name: dof.limits for dof in self.dofs}
 
         for run in range(runs):
             for dof in self.dofs:
                 offset = 0.25 * np.ptp(dof.limits) * np.random.uniform(low=-1, high=1)
-                dof.limits = (dof.limits[0] + offset, dof.limits[1] + offset)
+                dof.limits = (cache_limits[dof.name] + offset, cache_limits[dof.name] + offset)
 
             self.reset()
 
@@ -470,28 +471,13 @@ def acquisition_function_bounds(self):
         """
         Returns a (2, n_active_dof) array of bounds for the acquisition function
         """
-<<<<<<< HEAD
-<<<<<<< HEAD
-=======
->>>>>>> 6fffe33 (work at ATF on Oct 12)
         active_dofs = self.dofs.subset(active=True)
 
         acq_func_lower_bounds = [dof.lower_limit if not dof.read_only else dof.readback for dof in active_dofs]
         acq_func_upper_bounds = [dof.upper_limit if not dof.read_only else dof.readback for dof in active_dofs]
-<<<<<<< HEAD
-=======
-        acq_func_lower_bounds = [dof.lower_limit if not dof.read_only else dof.readback for dof in self.dofs]
-        acq_func_upper_bounds = [dof.upper_limit if not dof.read_only else dof.readback for dof in self.dofs]
->>>>>>> 39a579f (make sure DOF bounds are cast to floats)
-=======
->>>>>>> 6fffe33 (work at ATF on Oct 12)
 
         return torch.tensor(np.vstack([acq_func_lower_bounds, acq_func_upper_bounds]), dtype=torch.double)
 
-        return torch.tensor(
-            [dof.limits if not dof.read_only else tuple(2 * [dof.readback]) for dof in self.dofs.subset(active=True)]
-        ).T
-
     # @property
     # def num_objectives(self):
     #     return len(self.objectives)
@@ -669,8 +655,4 @@ def plot_validity(self, **kwargs):
             plotting._plot_valid_many_dofs(self, **kwargs)
 
     def plot_history(self, **kwargs):
-<<<<<<< HEAD
-        plotting._plot_history(self, **kwargs)
-=======
         plotting._plot_history(self, **kwargs)
->>>>>>> 39a579f (make sure DOF bounds are cast to floats)

bloptools/bayesian/objective.py

@@ -6,17 +6,8 @@
 
 numeric = Union[float, int]
 
-DEFAULT_MINIMUM_SNR = 2e1
-<<<<<<< HEAD
-<<<<<<< HEAD
-OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log", "noise"]
-
-=======
+DEFAULT_MINIMUM_SNR = 1e1
 OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log"]
->>>>>>> 39a579f (make sure DOF bounds are cast to floats)
-=======
-OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log", "noise"]
->>>>>>> 6fffe33 (work at ATF on Oct 12)
 
 
 class DuplicateKeyError(ValueError):
@@ -73,10 +64,6 @@ def __repr__(self):
     @property
     def noise(self):
         return self.model.likelihood.noise.item() if hasattr(self, "model") else None
-<<<<<<< HEAD
-
-=======
->>>>>>> 6fffe33 (work at ATF on Oct 12)
 
 
 class ObjectiveList(Sequence):

bloptools/bayesian/plotting.py

@@ -74,18 +74,9 @@ def _plot_objs_many_dofs(agent, axes=[0, 1], shading="nearest", cmap=DEFAULT_COL
     )
 
     agent.obj_axes = np.atleast_2d(agent.obj_axes)
-<<<<<<< HEAD
-<<<<<<< HEAD
 
     x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
-=======
-
-    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
->>>>>>> 6fffe33 (work at ATF on Oct 12)
-=======
 
-    x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
->>>>>>> 05b8e73 (fixed shapes in grid plots)
     x_values = agent.table.loc[:, x_dof.device.name].values
     y_values = agent.table.loc[:, y_dof.device.name].values
 
@@ -238,15 +229,7 @@ def _plot_acq_many_dofs(
 
     agent.acq_axes = np.atleast_1d(agent.acq_axes)
 
-<<<<<<< HEAD
-<<<<<<< HEAD
-    x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
-=======
-    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
->>>>>>> 6fffe33 (work at ATF on Oct 12)
-=======
     x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
->>>>>>> 05b8e73 (fixed shapes in grid plots)
 
     # test_inputs has shape (..., 1, n_active_dofs)
     test_inputs = agent.test_inputs_grid() if gridded else agent.test_inputs(n=1024)
@@ -309,15 +292,7 @@ def _plot_valid_many_dofs(agent, axes=[0, 1], shading="nearest", cmap=DEFAULT_CO
     if gridded is None:
         gridded = len(plottable_dofs) == 2
 
-<<<<<<< HEAD
-<<<<<<< HEAD
-    x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
-=======
-    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
->>>>>>> 6fffe33 (work at ATF on Oct 12)
-=======
     x_dof, y_dof = plottable_dofs[axes[0]], plottable_dofs[axes[1]]
->>>>>>> 05b8e73 (fixed shapes in grid plots)
 
     # test_inputs has shape (..., 1, n_active_dofs)
     test_inputs = agent.test_inputs_grid() if gridded else agent.test_inputs(n=1024)
@@ -417,4 +392,4 @@ def inspect_beam(agent, index, border=None):
 
     if border is not None:
         plt.xlim(x_min - border * width_x, x_min + border * width_x)
-        plt.ylim(y_min - border * width_y, y_min + border * width_y)
+        plt.ylim(y_min - border * width_y, y_min + border * width_y)

bloptools/tests/conftest.py

@@ -8,14 +8,8 @@
 from databroker import Broker
 from ophyd.utils import make_dir_tree
 
-<<<<<<< HEAD
-from bloptools.bayesian import Agent
-
-from .. import devices, test_functions
-=======
 from bloptools.bayesian import DOF, Agent, Objective
 from bloptools.utils import functions
->>>>>>> 05b8e73 (fixed shapes in grid plots)
 
 
 @pytest.fixture(scope="function")
@@ -64,13 +58,8 @@ def agent(db):
 
     agent = Agent(
         dofs=dofs,
-<<<<<<< HEAD
-        tasks=tasks,
-        digestion=test_functions.constrained_himmelblau_digestion,
-=======
         objectives=objectives,
         digestion=functions.constrained_himmelblau_digestion,
->>>>>>> 05b8e73 (fixed shapes in grid plots)
         db=db,
         verbose=True,
         tolerate_acquisition_errors=False,

bloptools/tests/test_acq_funcs.py

@@ -10,8 +10,4 @@ def test_analytic_acq_funcs_single_task(agent, RE, acq_func):
 @pytest.mark.parametrize("acq_func", ["qei", "qpi", "qem", "qucb"])
 def test_monte_carlo_acq_funcs_single_task(agent, RE, acq_func):
     RE(agent.learn("qr", n=32))
-<<<<<<< HEAD
     RE(agent.learn(acq_func, n=4))
-=======
-    RE(agent.learn(acq_func, n=4))
->>>>>>> a53c02b (parametrize acquisition function tests)

bloptools/tests/test_passive_dofs.py

@@ -11,15 +11,7 @@ def test_passive_dofs(RE, db):
         DOF(name="x2", limits=(-5.0, 5.0)),
         DOF(name="x3", limits=(-5.0, 5.0), active=False),
         DOF(BrownianMotion(name="brownian1"), read_only=True),
-<<<<<<< HEAD
-<<<<<<< HEAD
         DOF(BrownianMotion(name="brownian2"), read_only=True, active=False),
-=======
-        DOF(BrownianMotion(name="brownian2"), read_only=True),
->>>>>>> 39a579f (make sure DOF bounds are cast to floats)
-=======
-        DOF(BrownianMotion(name="brownian2"), read_only=True, active=False),
->>>>>>> 05b8e73 (fixed shapes in grid plots)
     ]
 
     objectives = [
@@ -39,4 +31,4 @@ def test_passive_dofs(RE, db):
 
     agent.plot_objectives()
     agent.plot_acquisition()
-    agent.plot_validity()
+    agent.plot_validity()

bloptools/utils/__init__.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 import botorch
 import numpy as np
 import scipy as sp
@@ -226,7 +225,3 @@ def best_image_feedback(image):
     yw = 2 * np.sqrt((np.sum(y_weight * y**2) / np.sum(y_weight) - y0**2))
 
     return x0, xw, y0, yw
-=======
-from .functions import *  # noqa F401
-from .misc import *  # noqa F401
->>>>>>> 05b8e73 (fixed shapes in grid plots)